Vegetable price prediction using data mining classification technique

2012 ◽  
Author(s):  
G. M. Nasira ◽  
N. Hemageetha
Keyword(s):  
Data Mining ◽  
Price Prediction ◽  
Classification Technique ◽  
Using Data

scholarly journals Agriculture Price Prediction Using Data Mining

2019 ◽  
Vol 8 (6S) ◽  
pp. 1075-1077
Keyword(s):  
Data Mining ◽  
Gross Domestic Product ◽  
Foreign Exchange ◽  
Vital Role ◽  
Individual Life ◽  
Price Prediction ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Agriculture Products ◽  
Mining Algorithms

Agriculture is the main source and backbone of Indian Economy and plays a vital role in individual life. In the total Gross Domestic Product (GDP) agriculture nearly contributes sixteen percent and for increasing foreign exchange it contributes nearly ten percent to the total country exports. As the population continuously increasing and to manage the livelihood of the country there’s requires a proper utilization and management of agriculture products. Data mining is a better technique and best choice in predicting the accurate prices of the agriculture based on previous data. In this work various data mining algorithms are applied on the dataset to predict the future prices of the agriculture products. Keywords: Agriculture, Data-Mining, Prediction.

A Study on Detection of Small Size Malicious Code using Data Mining Method

2019 ◽  
Vol 19 (1) ◽  
pp. 11-17
Author(s):  
Taek-Hyun Lee ◽  
◽  
Ho Kook Kwang
Keyword(s):  
Data Mining ◽  
Malicious Code ◽  
Mining Method ◽  
Data Mining Method ◽  
Using Data

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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